Establishing “is a” relationships for a taxonomy

ABSTRACT

Disclosed are methods for returning to a user an answer to the question “what is &lt;string&gt;.” Concepts and classes to which the concepts belong are determined from a corpus, such as taxonomy. The concepts are mapped to categories according to the structure of the taxonomy. Homonyms for words are collected and scored according to likeliness of use. Concept vectors are assembled for the identified concepts based on articles in the corpus and social media usage. Words are evaluated for generic-ness and a generic score is associated therewith. In responding to a query, the generic-ness of the terms of the query is evaluated and additional context solicited if the terms are generic. Candidate homonym concepts for a string in the query are selected according to context vectors for the homonym concepts. One or more homonym concepts are selected and the one or more categories corresponding to these concepts are returned.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 61/695,274, entitled WIKIGRAPH TO TREE MIGRATION PROVISIONAL, filedAug. 30, 2012, which is hereby incorporated herein by reference.

This application is related to U.S. application Ser. No. 13/622,975,filed Sep. 19, 2012, U.S. application Ser. No. 13/630,325, filed Sep.28, 2012 and U.S. application Ser. No. 13/630,369, filed Sep. 28, 2012.All applications are incorporated herein by reference for all purposes.

BACKGROUND

1. Field of the Invention

This invention relates to systems and methods for establishing “is a”relationships between terms of a query and a category in a taxonomy.

2. Background of the Invention

It is important in the art of search engines to provide results that arerelevant to an input query. Relevance of a search result is oftenmeasured by some sort of relatedness of terms in the query to a documentto be returned as a search result. For example, the inclusion of some orall of the terms in the query in the document may be used to measurerelevance. However, relevance is a very broad attribute and does notcontemplate the nature of the relationship between the query and thesearch result.

Disclosed herein are methods for establishing “is a” relationshipsbetween a search query and concepts in a taxonomy.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through use of theaccompanying drawings, in which:

FIG. 1 is a block diagram of a computing device suitable forimplementing embodiments of the present invention;

FIG. 2 is a block diagram of a network environment suitable forimplementing embodiments of the present invention;

FIG. 3 is a process flow diagram of a method for generating mappingsbetween concepts and categories in accordance with an embodiment of thepresent invention;

FIG. 4 is a process flow diagram of a method for identifying synonym andhomonyms lists in accordance with an embodiment of the presentinvention;

FIG. 5 is a process flow diagram of a method for generating a contextfor a concept in accordance with an embodiment of the present invention;

FIG. 6 is a process flow diagram of a method for evaluating thegeneric-ness of a term in accordance with an embodiment of the presentinvention; and

FIG. 7 is a process flow diagram of a method for responding to a queryin accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the Figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the invention, as represented in the Figures, is notintended to limit the scope of the invention, as claimed, but is merelyrepresentative of certain examples of presently contemplated embodimentsin accordance with the invention. The presently described embodimentswill be best understood by reference to the drawings, wherein like partsare designated by like numerals throughout.

The invention has been developed in response to the present state of theart and, in particular, in response to the problems and needs in the artthat have not yet been fully solved by currently available apparatus andmethods. In particular, the invention has been developed to provideapparatus and methods for establishing “is a” relationships betweenconcepts in a taxonomy and a category.

Embodiments in accordance with the present invention may be embodied asan apparatus, method, or computer program product. Accordingly, thepresent invention may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” Furthermore, the present invention may take the form of acomputer program product embodied in any tangible medium of expressionhaving computer-usable program code embodied in the medium.

Any combination of one or more computer-usable or computer-readablemedia may be utilized. For example, a computer-readable medium mayinclude one or more of a portable computer diskette, a hard disk, arandom access memory (RAM) device, a read-only memory (ROM) device, anerasable programmable read-only memory (EPROM or Flash memory) device, aportable compact disc read-only memory (CDROM), an optical storagedevice, and a magnetic storage device. In selected embodiments, acomputer-readable medium may comprise any non-transitory medium that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

Computer program code for carrying out operations of the presentinvention may be written in any combination of one or more programminglanguages, including an object-oriented programming language such asJava, Smalltalk, C++, or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on acomputer system as a stand-alone software package, on a stand-alonehardware unit, partly on a remote computer spaced some distance from thecomputer, or entirely on a remote computer or server. In the latterscenario, the remote computer may be connected to the computer throughany type of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).

The present invention is described below with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the invention. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions or code. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

FIG. 1 is a block diagram illustrating an example computing device 100.Computing device 100 may be used to perform various procedures, such asthose discussed herein. Computing device 100 can function as a server, aclient, or any other computing entity. Computing device can performvarious monitoring functions as discussed herein, and can execute one ormore application programs, such as the application programs describedherein. Computing device 100 can be any of a wide variety of computingdevices, such as a desktop computer, a notebook computer, a servercomputer, a handheld computer, tablet computer and the like.

Computing device 100 includes one or more processor(s) 102, one or morememory device(s) 104, one or more interface(s) 106, one or more massstorage device(s) 108, one or more Input/Output (I/O) device(s) 110, anda display device 130 all of which are coupled to a bus 112. Processor(s)102 include one or more processors or controllers that executeinstructions stored in memory device(s) 104 and/or mass storagedevice(s) 108. Processor(s) 102 may also include various types ofcomputer-readable media, such as cache memory.

Memory device(s) 104 include various computer-readable media, such asvolatile memory (e.g., random access memory (RAM) 114) and/ornonvolatile memory (e.g., read-only memory (ROM) 116). Memory device(s)104 may also include rewritable ROM, such as Flash memory.

Mass storage device(s) 108 include various computer readable media, suchas magnetic tapes, magnetic disks, optical disks, solid-state memory(e.g., Flash memory), and so forth. As shown in FIG. 1, a particularmass storage device is a hard disk drive 124. Various drives may also beincluded in mass storage device(s) 108 to enable reading from and/orwriting to the various computer readable media. Mass storage device(s)108 include removable media 126 and/or non-removable media.

I/O device(s) 110 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 100.Example I/O device(s) 110 include cursor control devices, keyboards,keypads, microphones, monitors or other display devices, speakers,printers, network interface cards, modems, lenses, CCDs or other imagecapture devices, and the like.

Display device 130 includes any type of device capable of displayinginformation to one or more users of computing device 100. Examples ofdisplay device 130 include a monitor, display terminal, video projectiondevice, and the like.

Interface(s) 106 include various interfaces that allow computing device100 to interact with other systems, devices, or computing environments.Example interface(s) 106 include any number of different networkinterfaces 120, such as interfaces to local area networks (LANs), widearea networks (WANs), wireless networks, and the Internet. Otherinterface(s) include user interface 118 and peripheral device interface122. The interface(s) 106 may also include one or more user interfaceelements 118. The interface(s) 106 may also include one or moreperipheral interfaces such as interfaces for printers, pointing devices(mice, track pad, etc.), keyboards, and the like.

Bus 112 allows processor(s) 102, memory device(s) 104, interface(s) 106,mass storage device(s) 108, and I/O device(s) 110 to communicate withone another, as well as other devices or components coupled to bus 112.Bus 112 represents one or more of several types of bus structures, suchas a system bus, PCI bus, IEEE 1394 bus, USB bus, and so forth.

For purposes of illustration, programs and other executable programcomponents are shown herein as discrete blocks, although it isunderstood that such programs and components may reside at various timesin different storage components of computing device 100, and areexecuted by processor(s) 102. Alternatively, the systems and proceduresdescribed herein can be implemented in hardware, or a combination ofhardware, software, and/or firmware. For example, one or moreapplication specific integrated circuits (ASICs) can be programmed tocarry out one or more of the systems and procedures described herein.

FIG. 2 illustrates an example of a computing environment 200 suitablefor implementing the methods disclosed herein. In some embodiments, aserver 202 a provides access to a database 204 a in data communicationtherewith. The database 204 a may store a directed graph. For example,the database 204 a may store the Wikipedia™ corpus or some other corpusof documents hyperlinked to one another to define a directed graph. Theserver 202 a may provide access to the database 204 a to various users.For example, the server 202 a may implement a web server for receivingrequests for data stored in the database 204 a and formatting requestedinformation into web pages. The web server may additionally be operableto receive information and store the information in the database 204 a.Although a single database 204 a and server 202 a are shown, the dataaccessed by users may be distributed across multiple databases 204 a andaccessed by means of multiple servers 202 a.

A server 202 b may be associated with another entity providinginformation services, such as responses to queries for information. Theserver 202 b may be in data communication with a database 204 b. Thedatabase 204 b may store information for use in responding to queries.In particular, the database 204 b may store a taxonomy obtained througha transformation of the directed graph from the databases 204 a. In someembodiment, both the directed graph and taxonomy are generated by thesame entity and stored and accessed using the same hardware. An operatormay access the server 202 b by means of a workstation 206, that may beembodied as any general purpose computer, tablet computer, smart phone,or the like.

The server 202 a and server 202 b may communicate with one another overa network 208 such as the Internet or some other local area network(LAN), wide area network (WAN), virtual private network (VPN), or othernetwork. A user may access data and functionality provided by theservers 202 a, 202 b by means of a workstation 210 in data communicationwith the network 208. The workstation 210 may be embodied as a generalpurpose computer, tablet computer, smart phone or the like. For example,the workstation 210 may host a web browser for requesting web pages,displaying web pages, and receiving user interaction with web pages, andperforming other functionality of a web browser. The workstation 210,workstation 206, servers 202 a-202 b, and databases 204 a, 204 b mayhave some or all of the attributes of the computing device 100.

FIG. 3 illustrates a method 300 for generating a mapping betweenconcepts and one or more categories according to an “is a” relationship,i.e., “<concept> is a <category>” or “Britney Spears” is a “musicalartist.” The method 300 may include generating 302 a corpus of strings,where each string represents a concept. A concept can be a person,animal, organism, activity, organization, or other physical or abstractentity. The concepts may be associated with articles or a corpus ofarticles in a reference work providing information on the concepts. Forexample, the corpus of articles may be an encyclopedia, such as theWikipedia™ corpus. In some embodiments, multiple corpuses of documentsmay be used to generate a collection of concepts. Any corpus ofdocuments may be used and multiple corpuses can be combined, such as aproduct catalog, dictionary or other reference corpus, a collection ofscholarly articles, and the like. For purposes of this disclosure, acorpus may be a combination of a number of corpuses of documents.

Generating 302 a string corpus may include extracting strings fromdocuments or sections of documents of the corpus that represent thesubject of the document or section, respectively. For example, titlefield of a document or section may be extracted as a concept string forthe string corpus.

The following steps of the method 300 may be used to identify a class towhich the concept associated with a concept string belongs for thestrings of the string corpus. For example, the method 300 may includeidentifying 304 nouns of opening sentences of the document correspondingto the concept. For example, in an encyclopedia, such as Wikipedia, thefirst sentence of an article is likely to specify a potential “is a”relationship for the concept that is the subject of the article becausethe first sentence defines what the concept ‘is.’ In some embodiments,one or more opening sentences may be processed using natural languageprocessing (NLP) to tag each word with a corresponding part of speech(noun, verb, preposition, adverb, etc.).

In some embodiments, analysis of the first sentence may proceed asoutlined below. The first few words (e.g. 3 or 4), of a sentence beforethe first verb such as a form of the verb “to be” may be ignored asunlikely to include a noun reflecting an “is a” relationship for theconcept. Following these words nouns may be identified but proper nounsdiscarded. The remaining nouns may be noted up until a comma following anoun other than a proper noun is found. The remaining nouns may then beused as candidate nouns that may have an “is a” relationship with theconcept. Adjectives applicable to the candidate nouns may be ignored. Inorder to facilitate later analysis, these candidate nouns may beevaluated with respect to synonym lists and a canonical form of thenouns chosen. The canonical form may simply be one of the synonyms thatis chosen to represent a particular group of synonyms. A synonym listmay be any such list generated in any manner. For example, Wordnet™provides lists of such synonyms.

The method 300 may further include evaluating 306 nouns in listings ofcategory parents for the concept. In some reference works, articles mayinclude listings of categories to which the concept belong. Theselistings may be a source of candidate nouns that may represent “is a”relationships for the concept. Accordingly, nouns referenced in listingof category parents may be identified according to NLP (natural languageprocessing) and used as candidate nouns. As for candidate nounsidentified in the opening sentence, the candidate nouns may be evaluatedwith respect to synonym lists and a candidate synonym selected in thesame manner. In particular, candidate nouns identified in the categoryparent listings may be compared with candidate nouns selected from theopening sentence as described above. A frequency with which nouns,including the candidate nouns from the opening sentence, occur in thenames of the category parents may be calculated. The candidate nounsfrom both the first sentence and category parent names may be used ascandidate classes for the concept. Nouns encountered in the firstsentence, category parents, or elsewhere may be de-pluralized prior tocomparisons.

In many reference corpuses, such as encyclopedias, there may beinfoboxes that include brief summaries of relevant facts for the conceptthat is the subject of an article. In particular, Wikipedia articlesoften include such infoboxes. In some reference corpuses, differentclasses of infoboxes are used for different types of articles. Forexample, the type of information listed in an infobox for a famousperson will be different than the type of information listed in aninfobox for a species of animal. Accordingly, the class of infobox maybe evaluated 308 and used as a candidate class for the concept.

The method 300 may further include evaluating 310 disambiguationresources for a concept. In some reference corpuses some portion of thecorpus or an article of the corpus may indicate possible meanings for aterm and reference articles for each of those meanings. For example,disambiguation of the term “apple” may include meanings “apple: fruit,”“apple: computer and software company,” and “apple: recording label.” Asshown in this example, a disambiguation text may include a briefexplanation or indicator of the meaning. This brief explanation orindicator may include a class identifier that can be used as a candidateclass for the concept. Accordingly, the method 300 may evaluate 310disambiguation resources by identifying the disambiguation textcorresponding to the concept and identifying nouns or class identifiersin this text and using the identified nouns or classes as candidateclasses for the concept. This may include selecting the canonicalsynonym for each of these candidate nouns or classes as described above.

Each of the candidate classes identified in the foregoing steps may beassigned 312 a score. The score may be based on where the candidateclass was identified, e.g., in the first sentence, infobox, categoryparents, or disambiguation text. In the case of candidate classesidentified in the first sentence, the score may additionally beaugmented according to occurrence of these classes in the textidentifying category parents. In some embodiments, candidate classes mayadditionally be scored according to the number of concepts mapped to thehierarchy. When establishing “is a” relationships, classes to which fewconcepts are mapped are typically not accurately characterized asrelating to these concepts in this manner. The number of concepts mappedto each class may be based on one or more of mappings of concepts toclasses in a first iteration of the method 300, a human generatedmapping of concepts to classes for a test set, the set of conceptshaving infobox classes associated therewith, or some other source ofmapping of concepts to classes.

A class of the candidate classes may then be selected and mapped 314 tothe concept. For example, the candidate class with the highest score, ascomputed by summing the above-referenced scores, may be mapped 314 tothe concept. In some embodiments, the infobox class may be a morereliable source of a class for a concept. Accordingly, if an infobox isfound in the article for the concept, the class of the infobox may bemapped 314 to the concept without regard to score, otherwise the highestscoring candidate class may be selected for mapping 314.

In some embodiments, infobox classes may be inferred 316 for conceptswith corresponding articles that lack infoboxes. For example, for eacharticle with an infobox class, attributes of the articles may be used totrain an algorithm to identify other articles that are likely to belongto the same infobox class. Any machine learning algorithm may be usedusing the articles with infoboxes as a training set. Likewise, anyattribute of an article may be used as an input to the algorithm. Forexample, the list of candidate classes as identified according to theforegoing steps may be used. Likewise, words occurring in the articlesanalyzed and contextual information may also be used. These inferredinfobox classes may be used to perform mapping 314 of a concept or forother use in other algorithms.

In some embodiments, the classes mapped 314 to a concept may be stringsthat are themselves subsequently mapped 318 to a specific category,where the category has a “is a” relationship to the concept. Thepossible categories may be a smaller taxonomy or hierarchy of conceptsthat are suitable for “is a” relationships with entities. In someembodiments, the concepts of a corpus may be organized into a hierarchyand the categories to which classes can be mapped may also be nodes inthis hierarchy. As an example, a taxonomy may be generated from a corpusof interconnected documents, such as Wikipedia or other corpus ofdocuments, using the methods disclosed in U.S. application Ser. No.13/622,975, filed Sep. 19, 2012, which is hereby incorporated herein byreference in its entirety.

In such embodiments, a class may be mapped 318 to one of thesecategories by selecting a node in the taxonomy of which N percent, e.g.80%, of the concepts mapped 314 to the class are descendants. Inaddition, or as an alternative, the category may be chosen as the nodeat which the number of concepts belonging to the class located under thenode is less than a threshold amount smaller than the number of conceptsmapped to the class that are located below a next higher node in thehierarchy. Other mapping schemes may also be used to map a class to acategory.

FIG. 4 illustrates a method 400 for assembling lists of synonyms andhomonyms associated with a concept for use in responding to queries asdescribed herein or for other purposes. The method 400 may includegenerating 402 a set of candidate synonyms. This may include usingexplicitly defined lists, such as the Wordnet synonym library. Candidatesynonyms may also be gathered from the reference corpus. In somereference works, such as an encyclopedia, resources are included toresolve synonyms. Accordingly, candidate synonyms may be extracted fromthese resources. In Wikipedia, for example, an article may include linksto other pages, some of which are redirects to synonyms that mightrepresent what a user is actually looking for. However, in someinstances these redirects may include links to related topics that arenot synonyms. Accordingly, concepts with redirects to a concept may beadded to the list of candidate synonyms for the concept and be subjectto a validation step 404. Synonyms derived from authoritative synonymlists may or may not be subject to the validation step 404. In someembodiments, only redirects, e.g., links, to separate articles may beadded to the candidate synonyms whereas links to a different portion ofthe article for a concept will not.

Various methods may be used to validate 404 a candidate synonym. Forexample, for the concept that is the subject of the method 400, a websearch for the canonical synonym representing the concept may beperformed. Web searches for the candidate synonyms may also beperformed. The search results for the canonical synonym and thecandidate synonyms may then be compared. In particular, one or both ofthe text of the search result entries and the URLs (uniform resourcelocators) of the search results. Each of the candidate synonyms mayreceive a score representative of the similarity of the text of searchresult entries to those of the canonical synonym. Another score may begenerated for a candidate synonym with a value corresponding tosimilarity of the URLs of the search results of the candidate synonym tothose of the canonical synonym. Another score for a particular candidatesynonym may also be generated in accordance with a number of occurrencesof the canonical synonym in the search results for that candidatesynonym. Some or all of these scores may be evaluated with respect toeach synonym and candidate synonyms kept as synonyms or discardedaccording to the score. For example, some or all of the three scores maybe summed, or weighted and summed, and the sum compared to a threshold.Those candidate synonyms with scores above the threshold may bevalidated and those with scores below the threshold discarded. Theauthoritative synonyms and validated synonyms may then be used as theset of synonyms for a concept.

The method 400 may further include determining homonyms for a string,such as a string corresponding to a particular concept or a synonym fora concept. This may include generating 406 a set of candidate homonyms.The set of homonyms may be derived from various sources includingauthoritative sets of homonyms from a reference. Candidate homonyms mayalso be derived from the corpus. For example, an encyclopedia, such asWikipedia, may include disambiguation resources that enable a user toselect a desired meaning for a string with multiple homonyms.Accordingly, meanings listed in these disambiguation resources may beadded to the set of candidate homonyms. The meanings may be mapped to acanonical synonym and the canonical synonym added to the list ofcandidate homonyms.

In instances where a string is very likely confused with a homonym, thearticle for a concept represented by the string may include a “hat note”that lists one or more alternative meanings, e.g. “this article is aboutapple, the fruit. For the company Apple, Inc., please see . . . . ” Themeanings listed in these hat notes may also be added to the set ofcandidate homonyms and may be given high priority. Articles may alsoinclude explicit clarifications of the meaning of a term, e.g., “X, notto be confused with X the Y.” Homonym meanings may therefore beextracted from these expressions as well and added to the set ofcandidate homonyms. In addition to exact matches (after depluralizationand removal of non-alphabetic characters, for example), meanings fornon-exact matching strings may also be considered as candidate homonymsbased on for example, a small edit distance from a string representing aconcept, being an expansion of an acronym, or an acronym or abbreviationof another term.

Candidate homonyms may be scored 408 and candidate homonyms that havelow scores, i.e., scores below a threshold, may be eliminated. Scoresfor a homonym may be a combination of a plurality of signals. Forexample, a signal may reflect a score based on where the homonym wasextracted. For example, homonyms extracted from hat notes may be given ascore (which may be relatively large due to the very indicative natureof hat notes), homonyms from disambiguation resources may be given adifferent score, and so on for the above-referenced sources of homonymmeanings. A score for a candidate homonym meaning may additionallyinclude a signal reflecting popularity of the meaning. For example,Wikipedia gathers data regarding views of articles in the corpus.Articles corresponding to particular candidate homonym meanings that arerarely accessed are less likely to be the meaning corresponding to astring with multiple homonyms. Accordingly, a signal corresponding tonumber of views of a Wikipedia article corresponding to a particularcandidate homonym meaning may also be used as part of the score for eachhomonym meaning.

The candidate homonym meanings for a string and their correspondingscores may be stored for later use. In some embodiments, only thehomonym meanings with scores above a threshold are stored for later useor only the homonyms with the top N scores are stored for a string forlater use.

FIG. 5 illustrates a method 500 for determining a context for a concept.The method 500 may include determining 502 usages of contextual wordsfrom the document corpus for the concept. For example, where there is amain article for a concept, the strings that occur in the article andthe number of times they occur may be added to a corpus concept vector,where each entry of the vector is a string and a frequency metric forthat string. In some embodiments, the value stored with each string maybe an inverse document frequency (IDF) score calculated as the number ofdocuments in the entire corpus divided by the number of documents inwhich the string occurs in the corpus.

The method 500 may additionally include determining 504 contextualstrings from social media postings. For example, the last N (e.g. 1000)documents generated on a social media site may be analyzed as a corpus.For example “documents” may include tweets on Twitter™ or postings onFacebook™, LinkedIn™, Foursquare, or the like. For example, concepts maybe identified in each document. This may include analyzing the documentto identify concepts using some or all of the methods described in U.S.patent application Ser. No. 13/300,524, entitled “PROCESSING DATAFEEDS,” filed Nov. 18, 2011, which is hereby incorporated herein byreference in its entirety.

For each identified concept, a social concept vector may be generated. Aconcept vector for a concept may list some or all words that occur indocuments referencing the concept along with a frequency metric. Thefrequency metric may be a ratio of the number of time a word occurs indocuments containing a concept divided by the total number of times theword occurs in the corpus. In some embodiment, only the top N words withthe highest frequency metric may be included in concept vector for aconcept. These top N words represent those words that are mostdistinctive for the concept.

The method 500 may additionally include determining 506 a lineagecontext vector for a concept. As noted above, a corpus may have a treestructure or be converted to a tree, such as a rooted, spanning treestructure. In such embodiments, word usage data may be generated for alineage based on a node and its descendants. For example, for a noderepresenting a class with sub-classes and entities as descendants, eachrepresented by a document such as a reference article, a concept vectorfor that node may be generated that includes a frequency metric for themost distinctive terms in the document for that node and all descendentnodes for the node. For example, a ratio of the number of times a wordis used in the node and its descendants may be divided by the totalnumber of times the word is used in the entire corpus may be used as thefrequency metric.

An aggregate concept vector may then be generated 508 for the conceptbased on some or all of the corpus concept vector, social conceptvector, and lineage concept vector. For example, a cosine ratio may becalculated for these vectors. The aggregate concept vector may have thesame form of the input vectors, namely a string with a frequency metricor score representing the affinity of the string to the concept withwhich the concept vector is associated. In some embodiments, logisticregression may be used to determine weightings for the frequency metricsdifferent concept vectors when combining them. In some embodiments,following aggregation of the three input vectors, the vector may bepruned to eliminate low scoring strings. For example, only the top Nstrings may be retained or only those strings with scores above athreshold. In addition to the aggregate vector, a popularity metric maybe stored along with the scores for each string. The popularity metricmay be a simple measure of the global popularity of each string in acorpus, such as a stream of social media documents as described above,the corpus of documents forming a reference work, or the like.Popularity may be a measure of the number of occurrences of the stringin one or more corpuses or the number of times documents containing thestring are accessed.

FIG. 6 illustrates a method 600 for evaluating how generic a string isfor use in responding to queries as described below. A generic term isone that does not communicate significant contextual information. Forexample terms such as “the,” “up” or “of,” etc. Generic terms may alsoinclude words that aren't simple parts of speech but that are socommonly used in so many contexts that they do not, of themselves,communicate any particular concept.

The method 600 may include evaluating 602 usages of strings in a corpus,evaluating 604 social media usage of the same strings, and comparing 606the usage in the corpus and in current social media content. Examples ofsocial media content include the last N (e.g. 1000) tweets on Twitter™or postings on Facebook™, LinkedIn™, Foursquare, or the like.

Evaluating 602, 604 usage of strings in the corpus and social media,respectively, may include evaluating context information for eachstring. For example, a vector may be generated for each string beingevaluated with the entries being strings, such as words, that occur inthe same documents as the string being evaluated and the frequency withwhich they co-occur with that string. In some embodiments, the frequencymay be the ratio of the number of times the strings of the vector occurwith the string being evaluated divided by the total number of times thestring of the vector occurs in the corpus or social media. In someembodiments evaluating 602, 604 usage of a string being evaluated mayinclude mapping the string to a concept and defining concept vectors forthe corpus and social media as described hereinabove. In suchembodiments, comparing 606 may include comparing the concept vectors ofthe corpus and the current social media content.

A string may be assigned a usage score that increases with thedifferences between the vectors, either the usage frequency vector orconcept vectors discussed above, of the current social media content andthe corpus. Alternatively, rather than a metric of how generic a stringis, the distinctiveness of a string may be measured, such that the usagescore would decrease with the differences between the vectors. Comparingthe vectors based on social media and the corpus may include for a givenstring in the vectors evaluating a difference in the scores in thevectors. The differences, or absolute values of the differences, foreach string in the vectors may then be summed as a metric of thesimilarity of usage of the string being evaluated.

The method 600 may further include evaluating 608 “linkworthiness” of aconcept. Determining linkworthiness of a string may include determininghow likely the word is to be included in a link, i.e., the text that auser clicks to navigate to a different web page. For example, alinkworthiness score for a string may be calculated as a ratio of thenumber of times a string is included in a link divided by the totalnumber of times the string is included in a corpus. For purposes oflinkworthiness, the corpus used may be a reference work or currentsocial media content, such as tweets or postings on one or more socialmedia sites.

The method 600 may further include evaluating 610 traffic for a stringin a corpus. For example, where a reference work is online, such asWikipedia, page views and other statistics may be published for articlesin the reference. Accordingly, the popularity, as measured by page viewsmay be retrieved and used to evaluate how generic a term is. In general,more popular articles are less likely to be generic.

The method 600 may include assigning 612 a generic score to a stringbased on analysis described above. For example, the difference scoredetermined at step 606, the linkworthiness score, and the popularityscore may be combined to determine a generic score. The weights appliedto each of these values may be determined according to logisticregression. For example, a set of test strings with generic scoresspecified by an analyst may be subject to the analysis of the method 600and logistic regression used to weight each of the values describedabove in order to reproduce accurate generic scores for the teststrings. The generic score as determined for each string may be storedin associate with the string for later use as described below. Inasmuchas the generic score depends in part on current social media content,the generic score may be updated periodically or for each queryreceived.

FIG. 7 illustrates a method 700 for responding to queries. The method700 may make use of pre-computed information in accordance with some orall of the foregoing methods. The method 700 may include receiving 702 aquery from a user, such as from a user's computing device. As notedabove, the methods described herein are particularly helpful fordetermining “is a” relationships. Accordingly, a query may be of theform “what is <string>.” A query may also include one or more termssupplied as context for <string>.

The words of the query may be evaluated 704 to determine whether thequery is generic. This may include evaluating a generic score of thewords of the query. A query may be deemed generic if none of the termsof the query have a generic score below a threshold, where a highergeneric score indicates a more generic term. Where a low score indicatesa term is more generic, a query may be deemed generic if none of theterms have a generic score above a threshold. In some embodiments, thegeneric-ness of a query may be characterized based on a combination ofthe generic score for the constituent terms of the query, for example, asum or average of the generic scores.

If the query is found 704 to be generic, then additional context may besolicited 706 and received from the querying user. A solicitation foradditional context may include displaying, or transmitting for display arequest for one or more words describing the context in which the queryterm or terms are used or to which they relate.

If the query is not found 704 to be generic, then the context of thequery may be evaluated with respect to pre-computed string contexts. Forexample, the <string> for which an “is a” relationship is requested mayhave a number of meanings associated therewith. Accordingly, thehomonyms associated with the string may be analyzed. In particular, theconcepts associated with each homonym may be retrieved and the conceptvectors, such as the concept vectors computed above, may be compared tothe contextual words provided with the query. Where the context of thequery has a high correlation to the context vector of a homonym meaningthen the meaning of <string> is apparent. As noted above, homonymmeanings may have a popularity score associated therewith. A <string>may be deemed to have an apparent meaning despite a lack of clearcorrelation with a particular concept vector if one of the homonymmeanings has a high popularity.

In some embodiments, evaluating whether a <string> has an apparentmeaning may include evaluating the context vectors for the meanings withrespect to the query context. For example, for each word in the querycontext that is included in the concept vector for a meaning, the scorefor that meaning may be augmented, either by a fixed amount or by thescore recorded in the concept vector for that word. The score for ameaning may also be augmented in accordance with the popularity of thathomonym meaning. The scores for each meaning may be one or both ofcompared to each other and to a threshold. For example, if a score for ameaning is above a threshold it may be selected as the apparent meaning.If there are multiple meanings with scores above the threshold, thescores may be compared to each other, if one of them is larger than theothers or is a threshold amount or percentage above the others, it maybe selected as the apparent meaning.

In any case, if a homonym meaning for <string> is found 710 to have anapparent meaning, then the category mapped to the concept of theapparent homonym meaning, as precomputed as described above, may beretrieved 712 and a response to the query is returned 714. Returning aresponse may include displaying or transmitting for display theretrieved 712 category. A query response may be formatted as “<string>is a <category>.” In some embodiments a confidence score correspondingto the score computed above for the apparent homonym meaning may bereturned as well.

If no apparent meaning for <string> is found 710, then top candidatehomonym meanings may be selected 716. This may include selecting thehomonym meanings with the top N scores based on the query context andthe concept vectors as described above. Alternatively, up to N homonymmeanings with scores above a threshold may be selected 716 as the topcandidates. The categories previously selected for the selected meanings(concepts) may then be retrieved 718 and returned 720 as a response tothe querying user by displaying or transmitting for display the selectedcategories. Returning 720 the categories for the selected meanings mayinclude returning a confidence score for each category. The confidencescore may be the score computed for the homonym meanings in order todetermine 710 an apparent meaning, or may be a function of this value.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrative,and not restrictive. The scope of the invention is, therefore, indicatedby the appended claims, rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A method establishing is-a relationships, themethod comprising: receiving, by a server, a query from a user, thequery including a string and one or more context words; generating afirst co-occurrence vector indicating co-occurrence statistics for termsin a reference corpus with respect to the string; generating a secondco-occurrence vector indicating terms and co-occurrence statistics forterms in current social media postings; determining a difference betweenthe first and second co-occurrence vectors; determining that thedifference between the first and second co-occurrence vectors exceeds athreshold condition; in response to determining that the differencebetween the first and second co-occurrence vectors exceeds a thresholdcondition, requesting from the user additional context words; retrievinghomonym concepts corresponding to the string and concept vectors foreach homonym concept; comparing the concept vectors to the one or morecontext words; selecting at least one homonym concept according to thecomparison; retrieving from a category-concept mapping database at leastone category for the selected at least one homonym concept, thecategory-concept mapping database establishing is-a relationshipsbetween a plurality of categories and a plurality of concepts; andtransmitting, by the server, the retrieved at least one category fordisplay to the user.
 2. The method of claim 1, further comprising:evaluating generic-ness of the string and zero or more context words;and if the string and zero or more context words are excessivelygeneric, soliciting more context words.
 3. The method of claim 1,further comprising generating the concept vectors for the homonymconcepts by, for each homonym concept: determining a first word usagefor an article in a reference corpus corresponding to the homonymconcept; determining a second word usage for documents using the homonymconcept in current social media documents; and combining the first andsecond word usages to generate the concept vector for the homonymconcept.
 4. The method of claim 3, wherein the first and second wordusage include first and second vectors, respectively, the first andsecond vectors having entries including words and a correspondingfrequency metric; and wherein combining the first and second word usagescomprises combining the first and second vectors.
 5. The method of claim4, wherein combining the first and second vectors comprises performing acosine ratio of the first and second vectors.
 6. The method of claim 4,wherein the reference corpus is a taxonomy and the article is a node inthe taxonomy.
 7. The method of claim 6, further comprising generating alineage vector by combining concept vectors for any descendent articlesof the article corresponding to the homonym concept in the taxonomy; andwherein combining the first and second word vectors to generate theconcept vector for the homonym concept further comprises combining thefirst and second vectors and the lineage vector.
 8. A system forestablishing is-a relationships, the system comprising one or moreprocessors and one or more memory devices operably coupled to the one ormore processors and storing executable and operational data effective tocause the one or more processors to: receive a query from a user, thequery including a string and one or more context words; generate a firstco-occurrence vector indicating co-occurrence statistics for terms in areference corpus with respect to the string; generate a secondco-occurrence vector indicating terms and co-occurrence statistics forterms in current social media postings; determine a difference betweenthe first and second co-occurrence vectors; determine that thedifference between the first and second co-occurrence vectors exceeds athreshold condition; in response to determining that the differencebetween the first and second co-occurrence vectors exceeds a thresholdcondition, request from the user additional context words; retrievehomonym concepts corresponding to the string and concept vectors foreach homonym concept; compare the concept vectors to the one or morecontext words; select at least one homonym concept according to thecomparison; retrieve from a category-concept mapping database at leastone category for the selected at least one homonym concept, thecategory-concept mapping database establishing is-a relationshipsbetween a plurality of categories and a plurality of concepts; andtransmit the retrieved at least one category for display to the user. 9.The system of claim 8, wherein the executable and operational data arefurther effective to cause the one or more processors to: evaluategeneric-ness of the string and zero or more context words; and if thestring and zero or more context words are excessively generic,soliciting more context words.
 10. The system of claim 8, wherein theexecutable and operational data are further effective to cause the oneor more processors to generate the concept vectors of the homonymconcepts by, for each homonym concept: determining a first word usagefor an article in a reference corpus corresponding to the homonymconcept; determining a second word usage for documents using the homonymconcept in current social media documents; and combining the first andsecond word usages to generate the concept vector for the homonymconcept.
 11. The system of claim 10, wherein the first and second wordusage include first and second vectors, respectively, the first andsecond vectors having entries including words and a correspondingfrequency metric; and wherein the executable and operational data arefurther effective to cause the one or more processors to combine thefirst and second word usages comprises combining the first and secondvectors.
 12. The system of claim 11, wherein the executable andoperational data are further effective to cause the one or moreprocessors to combine the first and second vectors by performing acosine ratio of the first and second vectors.
 13. The system of claim11, wherein the reference corpus is a taxonomy and the article is a nodein the taxonomy.
 14. The system of claim 13, wherein the executable andoperational data are further effective to cause the one or moreprocessors to generate a lineage vector by combining concept vectors forany descendent articles of the article corresponding to the homonymconcept in the taxonomy; and wherein the executable and operational dataare further effective to cause the one or more processors to combine thefirst and second word vectors to generate the concept vector for thehomonym concept by combining the first and second vectors and thelineage vector.
 15. A computer program product for establishing is-arelationships, the computer program product being embodied in anon-transitory computer readable storage medium and comprising computerinstructions for: receiving a query from a user, the query including astring and one or more context words; generating a first co-occurrencevector indicating co-occurrence statistics for terms in a referencecorpus with respect to the string; generating a second co-occurrencevector indicating terms and co-occurrence statistics for terms incurrent social media postings; determining a difference between thefirst and second co-occurrence vectors; determining that the differencebetween the first and second co-occurrence vectors exceeds a thresholdcondition; in response to determining that the difference between thefirst and second co-occurrence vectors exceeds a threshold condition,requesting from the user additional context words; retrieving homonymconcepts corresponding to the string and concept vectors for eachhomonym concept; comparing the concept vectors to the one or morecontext words; selecting at least one homonym concept according to thecomparison; retrieving from a category-concept mapping database at leastone category for the selected at least one homonym concept, thecategory-concept mapping database establishing is-a relationshipsbetween a plurality of categories and a plurality of concepts; andtransmitting the retrieved at least one category for display to theuser.
 16. The computer program product of claim 15, further comprisingcomputer instructions for: evaluating generic-ness of the string andzero or more context words; and if the string and zero or more contextwords are excessively generic, soliciting more context words.
 17. Thecomputer program product of claim 15, further comprising computerinstructions for generating the concept vectors of the homonym conceptsby, for each homonym concept: determining a first word usage for anarticle in a reference corpus corresponding to the homonym concept;determining a second word usage for documents using the homonym conceptin current social media documents; and combining the first and secondword usages to generate the concept vector for the homonym concept. 18.The computer program product of claim 17, wherein the first and secondword usage include first and second vectors, respectively, the first andsecond vectors having entries including words and a correspondingfrequency metric; and wherein combining the first and second word usagescomprises combining the first and second vectors.
 19. The computerprogram product of claim 18, wherein combining the first and secondvectors comprises performing a cosine ratio of the first and secondvectors.
 20. The computer program product of claim 18, wherein thereference corpus is a taxonomy and the article is a node in thetaxonomy.
 21. The computer program product of claim 20, furthercomprising computer instructions for generating a lineage vector bycombining concept vectors for any descendent articles of the articlecorresponding to the homonym concept in the taxonomy; and whereincombining the first and second word vectors to generate the conceptvector for the homonym concept further comprises combining the first andsecond vectors and the lineage vector.